Data Distribution Research Articles

×Gymnigritella suaveolens (Orchidaceae), a New Natural Hybrid to the Flora of Serbia

×Gymnigritella suaveolens (Orchidaceae), a natural hybrid between Gymnadenia conopsea and Nigritella rhellicani, was found at Mt Kopaonik for the first time in Serbia, representing its single known locality in the Central Balkans. Morphological characteristics, distribution data, habitat and ecological preferences are indicated. The hybrid was found at 1552 m a.s.l., on sericite schists, as a member of the Festuco-Deschampsietum flexuosae grassland community. Two hybrid specimens were recorded at the site where the two parental species grow in sympatry, and where the population size of G. conopsea was larger than the population size of N. rhellicani.

THE ODD LOMAX TOPP LEONE DISTRIBUTION: PROPERTIES AND APPLICATION

Lifetime distributions are parametric models used in statistical analyses of time-to-event data. Several probability distributions have been very used in literature to model lifetime data sets which have been useful in the analysis of lifetime data, but in most cases, they are not flexible enough to analyze some complex lifetime data in practice. Due to the importance of these lifetime distributions in modeling real lifetime data, there has several modifications and generalization of lifetime distributions, particularly the Lomax distribution to develop more flexible distributions to address the drawback presented by some classical lifetime distributions including the Lomax distribution. Several attempts have been made by researchers to generalize classical lifetime distributions which offer more flexibility in modeling lifetime data. Our interest in this study is to introduce a new extension of Lomax distribution called the “The odd lomax Topp-Leone distribution” which is bounded on a unit interval data such that its flexibility can accommodate increasing, decreasing, right skewed, left skewed, symmetric and u-shaped data sets. An application to a real lifetime data set clearly shows that the proposed extension of the Lomax distribution is a better alternative to some existing distributions bounded on a unit interval.

USING RETROSPECTIVE DATA TO ASSESS THE UNCERTAINTY OF INDICATORS OF BIOLOGICAL SAFETY OF FOOD AND FEED RAW MATERIALS

The results of the analysis of the advantages and disadvantages of the use of retrospective data for the implementation of the evaluation of uncertainty in the measurement of indicators of biological safety of food and feed raw materials are given. The model measurement procedure used quantitative determination of genetically modified organisms by means of polymerase chain reaction in real time 250 measurement results of certified reference material with a content of 0.1% genetically modified soybean line obtained under conditions of intra-laboratory reproducibility and 10 results of relevant inter-laboratory comparisons with the participation accredited provider. The suitability of retrospective data was assessed by the Shapiro-Wilk criterion and statistical controllability analysis using standardized score charts and modified Shewhart charts, uncertainty by 4 methods described in general and branch international guidelines and regulations. The results of using a combination of modified Shewhart maps demonstrate the acceptability of this approach for establishing the suitability of retrospective measurement results as input data for uncertainty assessment. By integrating additional "fitness limits" to a set of standardized control limits, this analysis acquires the ability to simultaneously establish not only stability, through the interpretation of geometric patterns, but also compliance with specific criteria. Using this approach, objective evidence of the suitability of retrospective data for estimating measurement uncertainty was obtained. The distribution of retrospective data obtained under conditions of intralaboratory reproducibility is assumed to be normal. As a result of the assessment of statistical controllability and compliance with the established criteria, objective evidence of the suitability of the data for uncertainty assessment was obtained. Analysis of uncertainty intervals of subgroups with the volume of 25 measurements, using 4 assessment methods, demonstrated significant variability in the results of measuring biological safety indicators. The uncertainty interval could narrow or widen by almost 100% of the value within fifty measurements. Median values obtained using different uncertainty estimation methods were 2.69%, 22.69%, 39.23, and 43.08%. This variability is due to the limited coverage of sources of uncertainty, which is inherent in each of the analyzed methods. The most optimal method is the evaluation based on the routine measurement of the certified reference material. Retrospective results of routine measurement of certified reference material and interlaboratory comparisons are an effective source of input data for assessing the uncertainty of the results of measuring biological safety indicators of agricultural raw materials. There is a need for further research to establish an assessment model that will provide an opportunity to take into account the optimal number of influential sources of uncertainty that are inherent in biological systems and cannot be simultaneously taken into account using existing approaches offered by standard assessment methods.

Multi-step reconstruction of radio-interferometric images

The large aperture arrays for the currently under construction SKA Observatory (SKAO) will allow for observations of the universe in the radio spectrum at unprecedented resolution and sensitivity. However, these telescopes will produce data on the scale of exabytes, introducing a slew of hardware and software design challenges. This paper proposes a multi-step image reconstruction framework that allows for partitioning of visibility data by baseline length. This enables more flexible data distribution and parallelization, aiding in processing radio-astronomical observations within given constraints. Additionally, as each step of the framework only relies on a subset of the total visibilities, one can perform reconstruction progressively, with the initial step performed on the SKAO Science Data Processors and the second on local clusters. The multi-step reconstruction is separated into two steps. First a low-resolution image is reconstructed with only short-baseline visibilities, and then using this image together with the long-baseline visibilities, the full-resolution image is reconstructed. The proposed method only operates in the minor cycle, and it can be easily integrated into existing imaging pipelines. We show that our proposed method allows for partitioning of visibilities by baseline without introducing significant additional drawbacks, reconstructing images of similar quality within similar numbers of major cycles compared to a single-step all-baselines approach that uses the same reconstruction method as well as compared to multi-scale CLEAN.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Comparison of Machine Learning Models and Feature Importance Investigation of Intelligent Fault Diagnosis Methods for Robots Based on Datasets Across Various Distributions

Intelligent fault detection is an important component of the industrial and automation fields. However, conventional research on intelligent fault detection mainly focuses on industrial production equipment, while there is little research on intelligent fault detection scenarios for robots. Based on a hexapod robot joints dataset, this paper investigates intelligent fault diagnosis tasks in the field of robotics. Firstly, the dataset is clustered into two new datasets with different distributions through k-means method, which is used to simulate the practicality of imbalanced distribution between healthy and fault data. Afterwards, several multioutput machine learning classification models were established to predict robot joints with faults. In two datasets with different distributions, the larger one is used as the training set and the smaller one is used as the testing set. The article compares the performance of these models with prediction accuracy as the main indicator. And based on the results, the paper selects the model with the highest accuracy for further exploration of feature importance. Finally, the article explains the significance of the results and analyzes possible reasons. The experimental results show that the random forest model better overcomes the problem of data distribution differences and has the highest accuracy. In the random forest model, the importance of position data representing position error is higher than that of slope with respect to the axis data representing angle error. This result may be related to the distribution of feature values and the fact that position data contains more crucial information.

A review on adversarial–based deep transfer learning mechanical fault diagnosis

Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advancement of artificial intelligence and industrial intelligence, industrial big data and its intelligent analysis provide robust support for fault prediction and health management of equipment. Building on existing research, intelligent diagnostics for mechanical equipment based on deep learning have gained significant attention and application. However, the success of big data relies on comprehensive fault data, which is challenging to obtain in practical applications where continuous equipment operation is essential. Moreover, mechanical equipment often operates under varying conditions, leading to different data distributions for training and testing. This discrepancy can result in low diagnostic accuracy or even failure of deep learning methods. Deep Transfer Learning (DTL) is an emerging machine learning paradigm that not only leverages the advantages of deep learning (DL) in feature representation but also harnesses the strengths of transfer learning (TL) in knowledge transfer. Consequently, DTL techniques can make deep learning-based fault diagnosis methods more reliable, robust, and applicable, leading to extensive development and research in the field of intelligent fault diagnosis. This paper primarily introduces adversarial-based deep transfer learning (ADTL) models, which are fundamentally based on Generative Adversarial Network (GAN). We provide a detailed discussion of the main applications of ADTL and its recent developments in intelligent fault diagnosis, along with some future challenges and prospects.

Applying minirhizotrons to observe spatiotemporal variations in rooting depth and distribution in agroecosystems to improve the performance of hydrological models

AbstractTo understand and explain soil moisture dynamics, the role of the vegetation is crucial. Hydrological processes in agricultural soils are strongly affected by rooting depth and root distribution. We present a new approach to monitor and model root dynamics and its influence on soil moisture using minirhizotrons combined with phenological observations. Field setups consist of a portable root scanner and acrylic glass tubes installed in the soil at the start of the growing season. 360° scans of soil and roots are taken regularly at different depths in the tubes. Root traits are identified automatically for each soil layer and complemented by observations of aboveground plant phenology. Results from minirhizotron data collected at an agrometeorological observatory in Germany show for both cereal and rapeseed (Brassica napus L.) crops a higher root density in deeper soil layers and a lower density near the soil surface when compared to literature data. An opposite picture emerged for maize (Zea mays L.) and potato (Solanum tuberosum L.), whereas vertical root distribution in sugar beet (Beta vulgaris subsp. vulgaris) had a different seasonal course than expected from literature. Applying the new root distribution data in calculations of the soil water balance resulted in differences of more than 3% in absolute volumetric soil water content. A comparison with in situ measurements of volumetric soil moisture at 20‐ and 50‐cm depth revealed a significant improvement of the model results due to the new parameterization. Thus, we argue that minirhizotrons constitute a useful supplement to hydrological observatories and can help understand and predict soil moisture dynamics in the critical zone.

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Effect of sacubitril/valsartan and dapagliflozin on athletic performance; can the popular cardiac medications of recent years be used as doping agents?

Abstract Background/Introduction Recent guidelines have highlighted the positive impacts of sacubitril/valsartan and dapagliflozin on heart failure management [1]. Given their potential misuse as doping agents in professional sports, this study evaluates their effects on athletic performance. Purpose To investigate the effects of sacubitril/valsartan and dapagliflozin on athletic performance and cardiac functions in a rodent model. Methods The study had a randomized double-blind design. 24 Sprague-Dawley male rats aged 4 months, divided into control, sacubitril/valsartan, and dapagliflozin groups. The rats were weighed at the start of the experiment. The medications (dapagliflozin 1,5 mg/kg/1 ml/once a day, sacubitril/valsartan 60 mg/kg/1 ml/once a day to their respective groups, and normal saline for control group) were administered via gastric gavage for 30 days. The rats were weighed, examined with transthoracic echocardiography and put on rotarod at the start of the experiment, and on 15th and 30th days. The rats were put on forced swimming tests for 20 days. Exhaustion, loss of coordinated movements and failure to surface for 3 seconds after submersion in the water were defined as indications for ending the test as per the guidelines regarding rat exercise testing [2]. M-mode and Doppler measurements, forced swimming test durations, rotarod durations and weight were recorded on 0th, 15th and 30th days. Results Initial performance metrics, including echocardiography, weight, and rotarod tests, showed no significant differences among the groups at baseline. The distributions in data were non-normal. Over the course of the study, median swimming times significantly differed from the 9th session, with dapagliflozin-treated rats exhibiting a substantial increase in endurance. Specifically, dapagliflozin group's swimming times increased to a median of 3580 seconds by the 20th session, compared to 2382 seconds in the control group and 2591 seconds in the sacubitril/valsartan group, indicating a pronounced improvement in exercise capacity (P<0.0001)(Figure-1). Echocardiographic assessments revealed enhanced cardiac output and ejection fraction, particularly in the sacubitril/valsartan group, suggesting increased cardiac efficiency and potential athletic performance enhancement. Conclusion(s) The study demonstrates a significant improvement in athletic performance with dapagliflozin compared to sacubitril/valsartan. These findings suggest a potential for misuse in sports doping, emphasizing the need for stringent regulation and monitoring within competitive athletics.Figure-1

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

MODELING FLOOD SUSCEPTIBILITY IN HYDROLOGICAL DATA-SCARCE STUDY AREAS

Hydrological information is essential for estimating the magnitude of floods, thus helping reduce losses and damage. However, when this data is scarce, it challenges flood modeling and risk assessment. In Brazil's west of Rio Grande do Sul, we used the Santa Maria hydrographic basin as a case study to estimate the river flow for different RP based on the statistical distribution of rainfall and river flow data and to simulate flood scenarios using a 2D IBER hydrological model.River flow estimates for 5, 10, 20, 30, and 50-year RP were obtained using the Giandotti equation. The results showed a high correlation between the river flows calculated from rainfall and the fluviometric data, using GEV and GumbelMax distribution. The areas susceptible to flooding are consistent with past flood events, validated with fieldwork and Sentinel imagery. River flows estimated from rainfall data can promote hydrological studies where fluviometric data is absent. Keywords: River Flow Estimation; Generalized Extreme Values; Flood Susceptibility; Rosário Do Sul; Brazil.

Optimizing Multivariate Time Series Forecasting with Data Augmentation

The convergence of data mining and deep learning has become an invaluable tool for gaining insights into evolving events and trends. However, a persistent challenge in utilizing these techniques for forecasting lies in the limited access to comprehensive, error-free data. This challenge is particularly pronounced in financial time series datasets, which are known for their volatility. To address this issue, a novel approach to data augmentation has been introduced, specifically tailored for financial time series forecasting. This approach leverages the power of Generative Adversarial Networks to generate synthetic data that replicate the distribution of authentic data. By integrating synthetic data with real data, the proposed approach significantly improves forecasting accuracy. Tests with real datasets have proven that this method offers a marked improvement over models that rely only on real data.

Feasibility of quantitative evaluation of hemodynamics using SPECT/CT in patients with chronic thromboembolic pulmonary hypertension

Abstract Background A ventilation/perfusion (V/Q) lung scan is recommended in the diagnostic work-up of patients with newly diagnosed pulmonary hypertension to rule out chronic thromboembolic pulmonary hypertension (CTEPH). V/Q single-photon emission computed tomography (SPECT) is considered the method of choice for diagnosis in CTEPH because it allows more precise evaluation than planar imaging. However, the evaluation of lung perfusion using V/Q SPECT imaging remains mainly a qualitative evaluation, and no quantitative method has been established. We hypothesized that the standardized uptake value (SUV) technique in Positron Emission Tomography (PET)-CT, a nuclear imaging technique well suited for quantitative of tumor biology, could be applied to quantitative assessment of lung perfusion. Purpose The purpose of this study was to evaluate the correlation between pulmonary perfusion distribution and hemodynamic data in Tc-labeled large-assembly albumin (99mTc-MAA) SPECT images in order to determine the feasibility of SPECT/CT for quantitative assessment of lung perfusion. Methods Twenty-five patients with CTEPH who underwent BPA and received SPECT-CT were included. Since the injected 99mTc-MAA selectively accumulates only in the lungs, SUV normalization was conducted using each patient’s lung volume, calculated by SPECT/CT and defined as SUV lung volume (LV) . The percentage of lung volume below each SUVLV value in total lung volume on SPECT/CT was examined before BPA, after two procedures of BPA, and six months after a series of BPA, and the correlations with hemodynamic parameters on right heart catheterization at each time point were evaluated. Results The Percent lung volume below SUVLV of 0.7 in total lung volume before BPA, after two procedures of BPA, and six months after a series of BPA had the strongest correlation with mean pulmonary artery pressure (mPAP) (rs; 0.727, p<0.001, Figure 1). The percent lung volume below SUVLV of 0.7 decreased with the BPA procedure in each patient with CTEPH (Figure 2). Furthermore, receiver operating characteristic analysis revealed that a threshold for the percent lung volume below SUVLV of 0.7 of 50.3% or greater could identify mPAP greater than 21 mmHg for right heart catheterization (area under the curve = 0.86, 95% CI 0.78-0.94, p<0.001). Conclusion Percent lung volume below SUVLV of 0.7 in total lung volume using SPECT/CT might reflect the severity of hemodynamics in patients with CTEPH. This method would be feasible for quantitative assessment of lung perfusion, even inter-patient and intra-patient.Figure 1Figure 2

Invasive quantitative assessment of coronary microvascular dysfunction in patients with hypertrophic cardiomyopathy

Abstract Background Patients (P) with hypertrophic cardiomyopathy (HCM) commonly suffer from coronary microvascular dysfunction (CMD) - the affection of function/structure of the coronary microvasculature. Invasive assessment of CMD through thermodilution methods allows for the calculation of coronary flow reserve (CFR) and index of microvascular resistance (IMR). These methods, while extensively studied in the context of non-obstructive coronary artery disease and myocardial infarction, have not been thoroughly validated in HCM P. Objectives To invasively assess the presence of CMD in P with HCM and to determine clinical predictors of invasively assessed CMD. Methods In a prospective single-centre study, we opportunistically recruited consecutive adult P with an established diagnosis of HCM with or without LVOTO that had an indication to pursue elective coronarography – such as pre-procedural evaluation for alcoholic septal ablation (ASA) or exclusion of epicardial coronary artery disease. P characteristics and coronary hemodynamic invasive assessment data were extracted. CFR was calculated as the ratio between resting and hyperemia mean transit times (TmnRest;TmnHyper). IMR was calculated as the ratio between distal coronary pressure (Pd) and the inverse of TmnHyper (IMR=Pd/TmnHyper-1). A cutoff of ≤22.0 in IMR, an ≥d 2 in CFR was used. We excluded P with end-stage HCM (LV EF <50% and/or or left ventricular wall thinning), or with epicardial coronary artery disease. Continuous variables were reported as mean ± SD or median and IQR depending on data distribution pattern and categorical data as frequencies and percentages. A linear regression model was used to test predictors of IMR and CFR involvement. Results 25 HCM P with a mean age of 63±10 years were included, 14 (56%) were female. In 20 (80%) of the P the cause for coronary angiography was ASA. All P referred for ASA had LVOTO and were on beta-blocker therapy (Table 1.). Median CFR was 2.5 [1.5-3] and median IMR was 19 [14-22], both within normal range. A total of 13 (44%) P had a reduced CFR and 5 (20%) of P had an increased IMR. 14 P (56%) had an altered CFR or IMR value, with the presence of a functional CMD phenotype in 10 P (40%) (Table 2). Angina was associated with a higher IMR (5.3, 95% CI - 0.05-10, p=0.053) and for each +1 increase in CCS a larger increase in the likelihood of a higher IMR was observed (CCS 1, +7 (95%CI 0.68 – 14); CCS 2, +9 (95%CI 4 – 14); CCS 3, 10 (95%CI 2 – 18)). A similar but non significant trend was observed for higher CCS correlating with lower CFR (Table 3). Conclusion In a prospective cohort of HCM P submited to invasive angiographic evaluation 56% had at least one invasive measure compatible with CMD. Angina, and a higher degree of anginal symptoms correlated positively with the presence of higher IMR and therefore with a higher level of CMD. In the future, we intend to correlate these findings with multimodality imaging/clinical outcomes.Patient characteristics and LR results

Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis

IntroductionMachine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored.MethodsWe used real-world data comparing antihypertensive users to non-users with 69 negative control outcomes, and plasmode simulations to study the performance of ML methods in PS and DRS estimation. We conducted a cohort study using UK primary care records. Further, we conducted a plasmode simulation with synthetic treatment and outcome mimicking empirical data distributions. We compared four PS and DRS estimation methods: 1. Reference: Logistic regression including clinically chosen confounders. 2. Logistic regression with L1 regularisation (LASSO). 3. Multi-layer perceptron (MLP). 4. Extreme Gradient Boosting (XgBoost). Covariate balance, coverage of the null effect of negative control outcomes (real-world data) and bias based on the absolute difference between observed and true effects (for plasmode) were estimated. 632,201 antihypertensive users and nonusers were included.ResultsML methods outperformed the reference method for PS estimation in some scenarios, both in terms of covariate balance and coverage/bias. Specifically, XgBoost achieved the best performance. DRS-based methods performed worse than PS in all tested scenarios.DiscussionWe found that ML methods could be reliable alternatives for PS estimation. ML-based DRS methods performed worse than PS ones, likely given the rarity of outcomes.

Temporal relationship between high-intensity training sessions and episodes of atrial fibrillation among previously inactive patients with paroxysmal or persistent AF

Abstract Background Exercise is considered beneficial for managing comorbidities in atrial fibrillation (AF) patients in the long term, and emerging evidence also indicate that high-intensity interval training (HIIT) may reduce AF burden and symptoms. However, more data on the impact of individual training sessions on recurrence of AF is needed to understand mechanisms and tailor exercise recommendations. Purpose To assess the temporal relationship between training sessions and recurrent AF episodes among previously inactive patients with paroxysmal or persistent AF being introduced to HIIT. Methods The Norwegian Exercise in Atrial Fibrillation Trial is a multi centre randomized controlled trial including inactive patients with paroxysmal or persistent AF, who are randomly assigned to either a structured exercise intervention or control for 12 months. We analysed AF episodes and training data from the initial weeks with introduction to structured exercise in the intervention group (8 supervised sessions of HIIT at 85-95% of maximal heart rate). AF episodes lasting ≥30 seconds were registered using implantable cardiac monitors (ICM). Training sessions were registered using sports watches and manual logs. We report event rates and rate ratios for recurrent AF episodes during HIIT compared to episodes occurring outside HIIT. Due to non-normal data distribution statistical comparisons were performed using the Wilcoxon sign-ranked test. Results 81 patients (24 female, 57 male), mean age of 64.2 years (range 35-78), were included. 57 patients had paroxysmal and 24 had persistent AF. The combined duration of monitoring was 5272 days (median 53, range 30 – 163). The participants performed a total of 418 hours of HIIT (median 5.3 hours, range 0.67 – 6.58) during 615 training sessions (median 8, range 1 – 8). In total 4528 AF episodes (median 4, range 0 – 1693) were registered, of which 18 occurred during HIIT. The mean AF rate per 24 hours was 0.69 outside HIIT, 1.20 during HIIT, 0.92 for the periods 1 – 3 hours and 4-6 hours after HIIT (Figure 1). The rate ratio for AF during HIIT compared to outside HIIT was 1.73 (p=0.002). In the 52 participants with at least one episode of AF during follow-up, the rate ratio for AF during HIIT compared to outside HIIT was 10.4 (bootstrapped 95% CI 0.2 to 30.3). Conclusion Among previously inactive patients with paroxysmal or persistent AF, the rate of recurrent AF episodes was significantly higher during high-intensity exercise than outside exercise. Given the few AF episodes, this mechanism of trigging AF by high-intensity exercise needs further investigation.Mean AF per 24 hours

Effect of body mass index on quadriceps angle and tibial torsion in school-aged children

BackgroundThe prevalence of excessive body mass (BMI) is increasing in pediatric population. Accordingly, the number of musculoskeletal problems in this population is also likely to increase. The quadriceps angle (Q-angle) is a clinical measure used to measure knee alignment in relation to the hip, femur, and tibia, as well as evaluating alignment of the patella. Tibial torsion (T.T) is an important morphological feature of human tibia. The aim of that study was to study the relationship between the Q-angle and TT in children 8 to 12 years old, the effect of BMI on Q-angle and T.T, and the difference in Q-angle and TT between dominant and non-dominant side.Results“Seventy-five children from primary schools” were enrolled in the study and allocated into 3 groups (25 subjects in each group), according to BMI: group I (normal weight = 25), group II (overweight = 25), and group III (obese = 25). The mean value of age of groups I, II, and III was 10.35 ± 1, 10.74 ± 1.2, and 10.17 ± 1.18 years respectively; the mean value of weight was 29.88 ± 3.94, 43.68 ± 6.55, and 50.06 ± 10.44 kg respectively; the mean value of height was 135.28 ± 7.63, 143.2 ± 8.85, and 140.72 ± 9.3 cm respectively; and the mean value of BMI was 16.22 ± 1, 21.18 ± 1.4, and 25.17 ± 2.67 kg/m2. The number (%) of boys in groups I, II, and III was 15 (60%), 18 (72%), and 17 (68%) respectively and of girls was 10 (40%), 7 (28%), and 8 (32%) respectively.The significant values were Q-angle and TT. Data were expressed as mean ± SD. ANOVA was used to compare between subjects’ characteristics of the groups. Kolmogorov-Smirnov test was used for testing normality of data distribution. One-way MANOVA was used to compare parametric variables (Q-angle and TT) between the two groups. Compared with normal weight children, there was statistically significant increase in Q-angle and TT angle in overweight and obese children.ConclusionAny change in BMI would eventually lead to change in both Q-angle and TT angle. The greater the BMI values, the greater the values of Q-angle and TT angle.

Association of soluble ST2 with heart failure and other comorbidities in patients with severe coronary heart disease

Abstract Background Circulating levels of soluble suppression of tumorigenicity-2 (sST2) reflect vascular congestion, inflammation, and fibrosis. sST2 has been proposed as a useful biomarker to predict the mortality and hospitalization due to heart failure. However, there is limited evidence regarding the relationship between sST2 and heart failure, as well as other comorbidities, in severe coronary heart disease (CAD). Purpose This study aims to measure the circulating levels of soluble ST2 (sST2) in a large population of patients with severe CAD to determine its association with heart failure, cardiac function, inflammation, and other important comorbidities. Methods This subanalysis is part of the ongoing perspective observational study. Available serum samples from 520 patients undergoing elective coronary artery bypass grafting (CABG) were analysed for sST2 levels. The inclusion criteria were written informed consent and age between 18-85 years. Exclusion criteria for this analysis were severe valvular disease, severe chronic obstructive pulmonary disease, oxygen therapy, nocturnal positive airway pressure support or mechanical ventilation, and the need for catecholamines or circulatory assist devices. The circulating levels of human sST2 were measured from frozen serum (-80°C) using commercially available ELISA kits. The plasma was diluted 100 times to detect sST2 levels (assay range of 31.2 - 2,000 pg/mL). Two measurements were taken to account as means for intra-assay variabilities. Logarithmic transformation was used to convert non-normal distributed data. Student t' test and linear regression were applied to categorical or continuous variables as appropriate. Results sST2 levels (median 20.4 ng/ml, [IQR 15.5 – 28.8]) significantly predict worse left ventricular ejection fraction (β = -0.164, p = 0.006), and significantly correlate with levels of NT-proBNP (β = 3.263, p < 0.001), hs-Troponin T (β = 3.391, p < 0.001), high-sensitivity C-reactive protein (β = 2.472, p = 0.014) and heart failure (p= 0.002). Interestingly, common comorbidities such as chronic kidney disease (estimated glomerular filtration rate < 60 mL/min/1.73 m2, p = 0.004), diabetes mellitus (p = 0.001) and higher levels of glycated haemoglobin (HbA1c, β = 3.615, p < 0.001) showed also a significant association with sST2 levels. No significant association of sST2 was observed with arterial hypertension (p = 0.190), atrial fibrillation (p = 0.289), obesity (BMI ≥ 30 kg/m2, p = 0.302), sleep apnoea (apnoea-hypopnoea index ≥ 15/h, p = 0.068), peripheral artery disease (p = 0.823), or previous stroke (p = 0.861). Conclusion(s) Circulating levels of sST2 significantly correlate with heart failure, NT-proBNP, hs-TnT, and hs-CRP in patients with severe CAD. Additionally, sST2 levels were found to be elevated in patients with diabetes mellitus and chronic kidney disease.